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In May 2015, our Team RBO won a prestigious international robotics challenge, the Amazon Picking Challenge. This challenge aims to solve one of the last problems in warehouse automation: identifying and grasping objects from a warehouse shelf.
Our robot was able to secure the lead by picking 10 out of 12 objects, outperforming 25 teams from Europe, USA and Asia, amongst them teams from the Massachusetts Institute of Technology (MIT), UC Berkeley as well as many robotics companies.
more to: Amazon Picking Challenge 2015

This project addresses a fundamental challenge in the intersection of machine learning and robotics. The machine learning community has developed formal methods to generate behaviour for agents that learn from their own actions. However, several fundamental questions are raised when trying to realize such behaviour on real-world robotics systems that shall learn to perceive, actuate and explore degrees of freedom (DoF) in the world. These questions pertain to basic theoretical aspects as well as the tight dependencies between exploration strategies and the perception and motor skills used to realize them.
more to: Physical Exploration Challenge

The main obstacle to a wide-spread adoption of advanced manipulation systems in industry is their complexity, fragility, lack of strength, and difficulty of use. This project describes a path of disruptive innovation for the development of simple, compliant, yet strong, robust, and easy-to-program manipulation systems. The idea is: Soft Manipulation (SoMa). The project is funded by European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 645599.
more to: Soft Manipulation (SOMA)

This project will develop robotics-specific machine learning methods. The requirement for such methods follows directly from the no-free-lunch theorems (Wolpert, 1996) which prove that no machine learning method works better than random guessing when averaged over all possible problems. The only way to improve over random guessing is to restrict the problem space and incorporate prior knowledge about this problem space into the learning method.
more to: Robotics-Specific Machine Learning (R-ML)

We investigate human and robot perception. The goal is to develop a constructive understanding of perceptual information processing, capitalizing on the analytic-synthetic loop. Our implementation of this concept is based on the concept of "optical cortex and robotic interactive perception algorithms". This resemblance is so striking because it spans various levels of abstraction and matches. Indeed, this computational architecture enables predictions that match observations in humans.
more to: Capabilities and consequences of recursive, hierarchical information processing in visual systems

Inspired by human grasping and manipulation capabilities, we will build anthropomorphic soft robotic hands that also act as a sensor to enable robust interactions with the environment.
Since they are made of soft materials, their morphology adapts to the environment which increases robustness and safety for human-robot interaction.
more to: Dexterous and Sensorized Soft Robotic Hands

The behavior of a soft robot is determined by the robot's shape and material properties, i.e. the robot's morphology. As soft robots come into contact with their environment, they deform, implicitly performing aspects of control, sensing, and actuation. Clever morphological design therefore favorably affects the robot's behavior while at the same time simplifying control and sensing.
In this project, we are co-designing the space of feedback control and morphology in the context of in-hand manipulation. Within the domain of in-hand manipulation, we develop computational tools and hardware components to support the design process, and we derive generalizable design insights that can transfer to other application of soft material robotics.
more to: Co-Design of Feedback Control and Soft Morphology for In-Hand Manipulation (SPP SMRS)

We research how birds and robots can learn to solve complex kinematic problems. To this end, we are building kinematic puzzles, called lockboxes and observe how goffin cockatoos can learn to solve these. Together with colleagues from Oxford and Vienna (where the bird experiments happen), we analyze the problem solving and learning behavior of these birds and try to extract information that may prove to be useful for a robotic approach to similar problems.
more to: Parrobots

The aim of this project is to investigate intelligent physical problem solving. Our example problem is an escape room scenario, where a robot needs to find a way to escape by physically interacting with the world (e.g. pull doors open, turn keys etc)
more to: Intelligent Kinematic Problem Solving